• Journal of Korean Society of Environmental Engineers
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• v.32 no.10
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• pp.916-927
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• 2010

In general water treatment process, the disinfection process by chlorine is used to prevent water borne disease and microbial regrowth in water distribution system. Because chlorines were reacted with organic matter, carcinogens such as disinfection by-products (DBPs) were produced in drinking water. Therefore, a suitable injection of chlorine is need to decrease DBPs. Rechlorination in water pipelines or reservoirs are recently increased to secure the residual chlorine in the end of water pipelines. EPANET 2.0 developed by the U.S. Environmental Protection Agency (EPA) is used to compute the optimal chlorine injection in water treatment plant and to predict the dosage of rechlorination into water distribution system. The bulk decay constant ($k_{bulk}$) was drawn by bottle test and the wall decay constant ($k_{wall}$) was derived from using systermatic analysis method for water quality modeling in target region. In order to predict water quality based on hydraulic analysis model, residual chlorine concentration was forecasted in water distribution system. The formation of DBPs such as trihalomethanes (THMs) was verified with chlorine dosage in lab-scale test. The bulk decay constant ($k_{bulk}$) was rapidly decreased with increasing temperature in the early time. In the case of 25 degrees celsius, the bulk decay constant ($k_{bulk}$) decreased over half after 25 hours later. In this study, there were able to calculate about optimal rechlorine dosage and select on profitable sites in the network map.

• Journal of Korean Society of Forest Science
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• v.90 no.2
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• pp.197-209
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• 2001

TOPMODEL, semi-distributed hydrological model, is frequently applied to predict the amount of discharge, main flow pathways and water quality in a forested catchment, especially in a spatial dimension. TOPMODEL is a kind of conceptual model, not physical one. The main concept of TOPMODEL is constituted by the topographic index and soil transmissivity. Two components can be used for predicting the surface and subsurface contributing area. This study is conducted for the validation of applicability of TOPMODEL at small forested catchments in Korea. The experimental area is located at Gwangneung forest operated by Korea Forest Research Institute, Gyeonggi-do near Seoul metropolitan. Two study catchments in this area have been working since 1979 ; one is the natural mature deciduous forest(22.0 ha) about 80 years old and the other is the planted young coniferous forest(13.6 ha) about 22 years old. The data collected during the two events in July 1995 and June 2000 at the mature deciduous forest and the three events in July 1995 and 1999, August 2000 at the young coniferous forest were used as the observed data set, respectively. The topographic index was calculated using $10m{\times}10m$ resolution raster digital elevation map(DEM). The distribution of the topographic index ranged from 2.6 to 11.1 at the deciduous and 2.7 to 16.0 at the coniferous catchment. The result of the optimization using the forecasting efficiency as the objective function showed that the model parameter, m and the mean catchment value of surface saturated transmissivity, $lnT_0$ had a high sensitivity. The values of the optimized parameters for m and InT_0 were 0.034 and 0.038; 8.672 and 9.475 at the deciduous and 0.031, 0.032 and 0.033; 5.969, 7.129 and 7.575 at the coniferous catchment, respectively. The forecasting efficiencies resulted from the simulation using the optimized parameter were comparatively high ; 0.958 and 0.909 at the deciduous and 0.825, 0.922 and 0.961 at the coniferous catchment. The observed and simulated hyeto-hydrograph shoed that the time of lag to peak coincided well. Though the total runoff and peakflow of some events showed a discrepancy between the observed and simulated output, TOPMODEL could overall predict a hydrologic output at the estimation error less than 10 %. Therefore, TOPMODEL is useful tool for the prediction of runoff at an ungaged forested catchment in Korea.

• Journal of Korean Society of Environmental Engineers
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• v.37 no.5
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• pp.253-261
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• 2015

In this study, Chlorophyll-a (chl-a) prediction model and multiple parameters affecting algae occurrence in Mulgeum site were evaluated by statistical analysis using water quality, hydraulic and climate data at Mulgeum site (1998~2008). Before the analysis, control chart method and effect period of typhoon were adopted for improving reliability of the data. After data preprocessing step two methods were used in this study. In method 1, chl-a prediction model was developed using preprocessed data. Another model was developed by Method 2 using significant parameters affecting chl-a after data preprocessing step. As a result of correlation analysis, water temperature, pH, DO, BOD, COD, T-N, $NO_3-N$, $PO_4-P$, flow rate, flow velocity and water depth were revealed as significant multiple parameters affecting chl-a concentration. Chl-a prediction model from Method 1 and 2 showed high $R^2$ value with 0.799 and 0.790 respectively. Validation for each prediction model was conducted with the data from 2009 to 2010. Training period and validation period of Method 1 showed 20.912 and 24.423 respectively. And Method 2 showed 21.422 and 26.277 in each period. Especially BOD, DO and $PO_4-P$ played important role in both model. So it is considered that analysis of algae occurrence at Mulgeum site need to focus on BOD, DO and $PO_4-P$.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• Clean Technology
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• v.28 no.1
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• pp.79-93
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• 2022

In this study, an electronics industrial wastewater activated sludge model (e-ASM) to be used as a Water Digital Twin was calibrated based on real high-tech electronics industrial wastewater treatment measurements from lab-scale and pilot-scale reactors, and examined for its treatment performance, effluent quality prediction, and optimal process selection. For specialized modeling of a high-tech electronics industrial wastewater treatment system, the kinetic parameters of the e-ASM were identified by a sensitivity analysis and calibrated by the multiple response surface method (MRS). The calibrated e-ASM showed a high compatibility of more than 90% with the experimental data from the lab-scale and pilot-scale processes. Four electronics industrial wastewater treatment processes-MLE, A2/O, 4-stage MLE-MBR, and Bardenpo-MBR-were implemented with the proposed Water Digital Twin to compare their removal efficiencies according to various electronics industrial wastewater characteristics. Bardenpo-MBR stably removed more than 90% of the chemical oxygen demand (COD) and showed the highest nitrogen removal efficiency. Furthermore, a high concentration of 1,800 mg L^-1 T MAH influent could be 98% removed when the HRT of the Bardenpho-MBR process was more than 3 days. Hence, it is expected that the e-ASM in this study can be used as a Water Digital Twin platform with high compatibility in a variety of situations, including plant optimization, Water AI, and the selection of best available technology (BAT) for a sustainable high-tech electronics industry.

• Korean Journal of Environmental Agriculture
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• v.38 no.4
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• pp.254-261
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• 2019

BACKGROUND: Agricultural water is known to be one of the major routes in bacterial contamination of fresh vegetable. However, there is a lack of fundamental data on the microbial safety of agricultural water in Korea. METHODS AND RESULTS: We investigated the density of indicator bacteria in the surface water samples from 31 sites collected in April, July, and October 2018, while the groundwater samples were collected from 20 sites within Jeollabuk-do in April and July 2018. In surface water, the mean density of coliform, fecal coliform, and Escherichia coli was 2.7±0.55, 1.9±0.71, and 1.4±0.58 log CFU/100 mL, respectively, showing the highest bacterial density in July. For groundwater, the mean density of coliform, fecal coliform, and E. coli was 1.9±0.58, 1.4±0.37, and 1.0±0.33 log CFU/ 100mL, respectively, showing no significant difference between sampling time. The survival of E. coli O157:H7 were prolonged in water with higher organic matter contents such as total nitrogen (TN), and nitrate-nitrogen (NO₃-N). The reduction rates of E. coli O157:H7 in the water showed greater in order of 25, 35, 5, and 15℃. CONCLUSION: These results can be utilized as fundamental data for prediction the microbiological contamination of agricultural water and the development of microbial prevention technology.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.3
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• pp.317-324
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• 2007

Fluorescence measurements of dissolved organic matter(DOM) have the superior advantages over other analysis tools for applying to water quality management. A preliminary study was conducted to test the feasibility of applying synchronous fluorescence measurements for tracing and monitoring pollution sources in a small stream located in an upstream area of the Sooyoung watershed in Busan. The water quality of the small stream is affected by leachate from sawdust pile and discharge of untreated sewage. The sampling sites included an upstream site, two pipes discharging untreated sewage, leachate from sawdust, and a downstream site. Of the five field samples, the leachate was distinguished from the other samples by a high peak at a lower wavelength range and a blunt peak at 350nm, suggesting that synchronous fluorescence can be used as a discrimination tool for monitoring the pollution. The efficacy of various indices derived from the spectral features to discriminate the pollution source was tested for well-defined mixture of the sawdust leachate and the upstream stream by comparing (1)the difference between measured values and those predicted based on mass balance and the characteristics of the two samples and (2)the linear correlations between index values and mass ratios of the sample mixtures. Of various discrimination indices selected, fluorescence intensities at 276 nm$({\Delta}\lambda=30nm)$and 347 nm$({\Delta}\lambda=60nm)$ were suggested as promising potential discrimination indices for the sawdust pollution source. Despite the limited number of samples and the study area, this study illustrates the evaluation process that should be followed to develop rapid, low-cost discrimination indices to monitor pollution sources based on end member mixing analyses.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.209-221
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• 2006

The purpose of this study is to analyze the effects of climate change on the nonpoint source pollution in a small watershed using a mid-range model. The study area is a basin in a rural area that covers 384 ha with a composition of 50% forest and 19% paddy. The hydrologic and water quality data were monitored from 1996 to 2004, and the feasibility of the GWLF (Generalized Watershed Loading function) model was examined in the agricultural small watershed using the data obtained from the study area. As one of the studies on climate change, KEI (Korea Environment Institute) has presented the monthly variation ratio of rainfall in Korea based on the climate change scenario for rainfall and temperature. These values and observed daily rainfall data of forty-one years from 1964 to 2004 in Suwon were used to generate daily weather data using the stochastic weather generator model (WGEN). Stream runoff was calibrated by the data of $1996{\sim}1999$ and was verified in $2002{\sim}2004$. The results were determination coeff, ($R^2$) of $0.70{\sim}0.91$ and root mean square error (RMSE) of $2.11{\sim}5.71$. Water quality simulation for SS, TN and TP showed $R^2$ values of 0.58, 0.47 and 0.62, respectively, The results for the impact of climate change on nonpoint source pollution show that if the factors of watershed are maintained as in the present circumstances, pollutant TN loads and TP would be expected to increase remarkably for the rainy season in the next fifty years.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Korean Journal of Environment and Ecology
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• v.21 no.4
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• pp.311-317
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• 2007

This study was conducted to determine the geographical distribution and habitat for Rana catesbeiana and Trachemys scripta elegans in order to obtain the current basic data on biota needed for establishing a database on non-indigenous species(NIS) in Jeju-do and to develop management plans applicable to Jeju, starting from March 2005 until May 2006. As a result of an inspection, it was found that the habitat for R. catesbeiana in Jeju includes Hankyeong-myeon, Daejeong-eup, and Ahndeok-myeon. These areas are all located on the western side of Jeju-do, and considered to have favorable conditions for R. catesbeiana to inhabit. Thus, it is unlikely that the population of R. catesbeiana will possibly decrease; what's worse, there exists even a risk that it could not only expand to other regions but also do damage to the population of native insecta, fishes, amphibians and reptiles, so this terrible species ought to be exterminated. More than 92 individuals of T. scripta elegans species were found to inhabit in 15 ponds around Jeju Island. T. scripta elegans. as the topmost predator, could destruct the pond ecosystem, and also cause a problem of water pollution due to its excrement. Counter-measures against T. scripta elegans could include publicity activities encouraging self-control of the release of captive T. scripta elegans, extermination plans using its ecological habits, and sign-posting publicizing its hazard to healthy ecosystem at the areas with high population density of T. scripta elegans or at its habitats with high foot traffic, together with the continuous management of NIS(none-indigenous species).